Generalized Example-Based Machine Translation

1. What is Example-Based Machine Translation?

Example-based translation is essentially translation by analogy. An
Example-Based Machine Translation (EBMT) system is given a set of
sentences in the source language (from which one is
translating) and their corresponding translations in the target
language, and uses those examples to translate other, similar
source-language sentences into the target language. The basic premise
is that, if a previously translated sentence occurs again, the same
translation is likely to be correct again.

Which example(s) an EBMT system determines to be equivalent (or at
least similar enough) to the text to be translated varies according
to the approach taken by the system. This will be discussed in more
detail on the next page.

A restricted form of example-based translation is available
commercially, known as a translation memory. In a translation memory,
as the user translates text, the translations are added to a database,
and when the same sentence occurs again, the previous translation is
inserted into the translated document. This saves the user the effort
of re-translating that sentence, and is particularly effective when
translating a new revision of a previously-translated document
(especially if the revision is fairly minor).

More advanced translation memory systems will also return close but
inexact matches on the assumption that editing the translation of the
close match will take less time than generating a translation from
scratch.